Computes the rolling average of values forward or backward of the current row within the specified column.

If an input value is missing or null, it is not factored in the computation. For example, for the first row in the dataset, the rolling average of previous values is the value in the first row.

The row from which to extract a value is determined by the order in which the rows are organized based on the order parameter.

If you are working on a randomly generated sample of your dataset, the values that you see for this function might not correspond to the values that are generated on the full dataset during job execution.

The function takes a column name and two optional integer parameters that determine the window backward and forward of the current row.

The default integer parameter values are -1 and 0, which computes the rolling average from the current row back to the first row of the dataset.

col_ref

Name of the column whose values are used to compute the rolling average.

Multiple columns and wildcards are not supported.

Usage Notes:

Required?

Data Type

Example Value

Yes

String (column reference to Integer or Decimal values)

myColumn

rowsBefore_integer, rowsAfter_integer

Integers representing the number of rows before or after the current one from which to compute the rolling average, including the current row. For example, if the first value is 5, the current row and the four rows after it are used in the computation. Negative values for k compute the rolling average from rows preceding the current one.

rowBefore=1 generates the current row value only.

rowBefore=-1 uses all rows preceding the current one.

If rowsAfter is not specified, then the value 0 is applied.

If a group parameter is applied, then these parameter values should be no more than the maximum number of rows in the groups.

Usage Notes:

Required?

Data Type

Example Value

No

Integer

4

Examples

Example - Compute prior quarter values

The following dataset contains order information for the preceding 12 months. You want to compare the current month's average against the preceding quarter.

Source:

Date

Amount

12/31/15

118

11/30/15

6

10/31/15

443

9/30/15

785

8/31/15

77

7/31/15

606

6/30/15

421

5/31/15

763

4/30/15

305

3/31/15

824

2/28/15

135

1/31/15

523

Transform:

Using the ROLLINGAVERAGE function, you can generate a column containing the rolling average of the current month and the two previous months:

window value: ROLLINGAVERAGE(Amount, 3, 0) order: -Date

Note the sign of the second parameter and the order parameter. The sort is in the reverse order of the Date parameter, which preserves the current sort order. As a result, the second parameter, which identifies the number of rows to use in the calculation, must be positive to capture the previous months.

Technically, this computation does not capture the prior quarter, since it includes the current quarter as part of the computation. You can use the following column to capture the rolling average of the preceding month, which then becomes the true rolling average for the prior quarter. The window column refers to the name of the column generated from the previous step:

window value: NEXT(window, 1) order: -Date

Note that the order parameter must be preserved. This new column, window1, contains your prior quarter rolling average:

rename col:window1 to:'Amount_PriorQtr'

You can reformat this numeric value:

set col:Amount_PriorQtr value:NUMFORMAT(Amount_PriorQtr, '###.00')

You can use the following transform to calculate the net change. This formula computes the change as a percentage of the prior quarter and then formats it as a two-digit percentage.

NOTE: You might notice that there are computed values for Amount_PriorQtr for February and March. These values do not factor in a full three months because the data is not present. The January value does not exist since there is no data preceding it.

Date

Amount

Amount_PriorQtr

NetChangePct_PriorQtr

12/31/15

118

411.33

-71.31

11/30/15

6

435.00

-98.62

10/31/15

443

489.33

-9.47

9/30/15

785

368.00

113.32

8/31/15

77

596.67

-87.1

7/31/15

606

496.33

22.1

6/30/15

421

630.67

-33.25

5/31/15

763

421.33

81.09

4/30/15

305

494.00

-38.26

3/31/15

824

329.00

150.46

2/28/15

135

523.00

-.74.19

1/31/15

523

Example - Rolling window functions

This example describes how to use the rolling computational functions:

ROLLINGSUM - computes a rolling sum from a window of rows before and after the current row. See ROLLINGSUM Function.

ROLLINGAVERAGE - computes a rolling average from a window of rows before and after the current row. See ROLLINGAVERAGE Function.

ROWNUMBER - computes the row number for each row, as determined by the ordering column. See ROWNUMBER Function.

The following dataset contains sales data over the final quarter of the year.

Source:

Date

Sales

10/2/16

200

10/9/16

500

10/16/16

350

10/23/16

400

10/30/16

190

11/6/16

550

11/13/16

610

11/20/16

480

11/27/16

660

12/4/16

690

12/11/16

810

12/18/16

950

12/25/16

1020

1/1/17

680

Transform:

First, you want to maintain the row information as a separate column. Since data is ordered already by the Date column, you can use the following:

window value:ROWNUMBER() order:Date

Rename this column to rowId for week of quarter.

Now, you want to extract month and week information from the Date values. Deriving the month value:

derive type:single value:MONTH(Date) as:'Month'

Deriving the quarter value:

derive type:single value:(1 + FLOOR(((month-1)/3))) as:'QTR'

Deriving the week-of-quarter value:

window value:ROWNUMBER() order:Date group:QTR

Rename this column WOQ (week of quarter).

Deriving the week-of-month value:

window value:ROWNUMBER() group:Month order:Date

Rename this column WOM (week of month).

Now, you perform your rolling computations. Compute the running total of sales using the following:

window value: ROLLINGSUM(Sales, -1, 0) order: Date group:QTR

The -1 parameter is used in the above computation to gather the rolling sum of all rows of data from the current one to the first one. Note that the use of the QTR column for grouping, which moves the value for the 01/01/2017 into its own computational bucket. This may or may not be preferred.

Rename this column QTD (quarter to-date). Now, generate a similar column to compute the rolling average of weekly sales for the quarter:

In this example, the following data comes from times recorded at regular intervals during a three-lap race around a track. The source data is in cumulative time in seconds (time_sc). You can use ROLLING and other windowing functions to break down the data into more meaningful metrics.

lap

quarter

time_sc

1

0

0.000

1

1

19.554

1

2

39.785

1

3

60.021

2

0

80.950

2

1

101.785

2

2

121.005

2

3

141.185

3

0

162.008

3

1

181.887

3

2

200.945

3

3

220.856

Transform:

Primary key: Since the quarter information repeats every lap, there is no unique identifier for each row. The following steps create this identifer:

settype col: lap,quarter type: 'String'

derive type:single value: MERGE(['l',lap,'q',quarter]) as: 'splitId'

Get split times: Use the following transform to break down the splits for each quarter of the race: